The thinking machine's man … or woman

By comparing the distant past with the economic-crisis-ridden present, Anita Hawser argues, lessons can be learned for the future deployment of automated and algorithmic trading functionality. Should machines be taught to discuss their feelings, or are they better off leaving the sentiment to us and sticking to the numbers?

Way back in the
sixties, as more and more powerful computers were being
developed, computer scientists were beginning to ask themselves
whether, one day, in the far distant future, perhaps in the
eighties or even beyond, computers would be "fully conversant",
by which they meant, capable of acting with "human-like"
intelligence.

Here in the distant future, in the time beyond such science
fiction, we know that, just as computers occasionally win chess
games, so algorithms are increasingly sophisticated and
increasingly being deployed in trading rooms to finesse
increasingly sophisticated trades. A number of trading firms are
also using "machine-learning" techniques to optimise trading
strategies, says Graham Miller, CEO, Marketcetera. "Machine
learning techniques are good at taking a trading strategy and
squeezing an extra 40% to 50% out of it," Miller explains.

Graham Miller

But Richard Balarkas, president and CEO of Instinet Europe,
traces the rise of the machines in the trading room back to
decimalisation of the US capital markets in 2000, rather than
back to those clunky early thinking machines. The point about
decimalisation is that it resulted in smaller tick and trade
sizes, causing market data volumes to explode, and the point
there is that machine development changed course towards
number-handling, frequency, volume, and so on, and thus away from
more intuitive, interpretative approaches. As Balarkas points
out, the increased data volumes were way beyond the data
assimilation capabilities of a human trader, whereas machines
were able to process thousands of data points per second as
inputs to high frequency auto/algo models without difficulty.

So the change of direction was self-reinforcing; once those
machines had proved themselves better at handling data, that's
how they had to develop. The machine became an integral part of
every trader's kit, allowing them to analyse vast reams of market
data and deploy increasingly sophisticated trading strategies or
algorithms. So the evolution had to continue, further away from
that old concept of "full conversance" and on into ever-faster
data exploitation. "A lot of these trading strategies are time
sensitive which meant five years ago any trading opportunity in
the market place was only there for a few seconds," says Miller.
"Today, those opportunities disappear in under a millisecond."

Richard Balarkas

Machines are essential for traders to capitalise on such
opportunities, of course, and they provide a rich array of
"non-human" functionality. "[They] are also better with a certain
complexity of calculation, things like ETF trades where you need
to calculate fair value," explains Miller. "And to the extent
that many risk metrics require significant computation, you can
also put together a system that manages risk without much human
intervention." Given the volatility of stock markets in recent
months, with swings of 700 bps to 1200 bps in major market
indices and spikes in trading volumes, some even venture to
suggest that "having an autopilot [or machine-based model] at the
controls has been a generally safer bet than being in one guided
by human hands."

The road not travelled, in this context, is the one on which
desktop trading systems evolved to the point at which they could
talk back, discuss trade ideas, effectively "feel" the market in
the way that a human trader might, and possibly even provide
feedback. That's not totally science fiction, in a world where
the competition between machine-readable news feeds is beginning
to hinge on the capacity, first, to react to sentiment, and
secondly, to measure the possible impact of non-market news (see
Automated Trader Q4 2008, cover story). Bear that in mind as we
discuss whether the ongoing economic meltdown has provided an
effective tipping point in favour of machines and at the expense
of human traders. Have the machines "won" the crisis, and if so,
would they have won bigger if they'd been more human?

Hitesh Mittal

Initial evidence favours the machines. "In this challenging
environment, selecting the right algorithm can help traders
efficiently manage risk and volatility," says Hitesh Mittal,
managing director and head of algorithmic trading at Investment
Technology Group. ITG's analysis of 2008 client execution data
for 3.5 billion share trades also indicates that choosing the
'right' algorithm during highly volatile market conditions saved
trading customers "up to 60 bps in trading costs, as compared to
a more modest but still significant 20 bps of cost savings during
low volatility periods".

Volatility is good, of course, and not only if you're choosing
the right algorithm every time. "I have a friend who is machine
trading and he has been making more or less consistent profits
all through the crisis while the rest of his trading desk - very
experienced people, but all manual - have had a very difficult
end of 2008," says Jonas Hansbo, CEO, Tbricks. "Given recent
market conditions, a lot of people have quite successfully
managed to continue using their existing code without major
rewrites." But not everybody agrees with that, or sees 2008 as a
win for the machines. Miles Kumaresan, head of Quantitative
Trading at TransMarket Group, does not believe machines
outperform humans, even in volatile market conditions. What is
more important, he says, is the ability of the trading model to
adapt to varied market conditions dynamically. "If you used vast
amounts of data to create a model that can calibrate itself and
respond optimally to significant changes such as market
volatility, then the model will perform a lot better," Kumaresan
says.

That almost sounds like using vast amounts of data to achieve
full conversance. But not quite. In terms of risk adjusted
returns, Kumaresan says quantitative trading strategies have
performed significantly better than the alternatives in the
current climate. In this respect, he argues that the benefit a
machine brings is that it enables a trader to analyse data more
accurately and more quickly. Back to the numbers.
Frédéric Ponzo, managing director, NET2S, says that
market conditions over the last few months have supported those
trading models based on volatility. However, it is not because
they have got more computers doing the job that these strategies
are "winning", he says. "It is because they are backing the right
strategy."

Miles Kumaresan

Again, volatility is good, and especially so if you're using the
right "volatility-friendly" model. Going back to the man/machine
debate, Bruce Bland, head of algorithmic research at Fidessa,
argues machines have a number of factors in their favour. "They
are not emotional about gains and losses. They simply maintain
the trading style they were set, and continue, regardless of the
fact that they are winning or losing." They never take lunch or a
break, or get a phone call. "Humans are subject to many more
sources of information which may help trading, but may also
hinder trading performance." And they're fast. "With the growth
in smart order routing, this is becoming more important, with
machines able to pick up price improvement from non-primary
markets especially in volatile periods."

There's another rather more depressing argument against filling
up the trading floor with warm bodies. This is a time of staff
cutbacks. "There is a case for increasing automation because it
increases productivity," says Ponzo of NET2S. "Machines can
execute more trades, look at more stocks and contemplate more
scenarios." But what does this mean for the surviving human
traders? Will machines control trading as well as execute trades,
much as the on-board computer HAL controlled the space ship's
operations in Stanley Kubrick's film,"2001: A Space Odyssey"?

No. "Due to the chaotic nature of markets, it is hard to imagine
a trading world completely devoid of humans, although their role
may lie in supervising models," says Bland. Others are less
bullish about the pervasiveness of machines in the trading room,
particularly for those trades that do not lend themselves to
automation - and there still are some. Scott Eaton, former global
head of Principal Trading at ABN Amro says there is something to
be said for "warm" equity trading. "Trading credit, for example,
is more art than science," he says. Even now, or perhaps,
especially now.

Frédéric Ponzo

"There is always a need for the human interpretation of
information," says Balarkas, recalling an interview he gave some
years back where he was asked about the use of artificial
intelligence in the trading room. "I said we are still looking
for the natural stuff. The sentiment I was trying to get across
is that the machines are
only as good as the humans that make them." And sometimes the
human programmers make erroneous judgements, misread the market
or fail to anticipate certain events. While a number of high
frequency statistical arbitrage models have performed well in the
current climate, Balarkas points out that some models were
wrecked by high levels of volatility, which no one had predicted.
"Although these models were designed to accommodate rigorous back
testing and highly complex Monte Carlo simulations, inevitably
nobody was considering the likelihood of scenarios that hadn't
happened before," he says.

Yet, today's high velocity markets require seasoned traders to
leverage fully the capabilities of automated systems, says Miller
of Marketcetera. He cites that famous incident last year when a
reporter re-posted a 2002 United Airlines bankruptcy filing,
flagging the story as new and feeding it into a wire service
published on thousands of Bloomberg terminals worldwide. "Both
automated systems and their human counterparts got it wrong and
triggered waves of selling," says Miller. "A carefully calibrated
strategy, however, fully leveraging the power of the machine,
would have searched archival records and caught the anomaly
before executing a trade."

David Knox

Sometimes the capabilities of the machine and the human
programming it are so closely intertwined that it is difficult to
determine where one begins and the other ends, but as Ponzo
points out, the machine is merely an extension of the trader
rather than vice-versa. Yet, without machines, traders would not
be able to execute complex algorithms quickly enough to
capitalise on market opportunities, let alone refine them. "The
other benefit I think that is often overlooked is that when you
use consistent models and algorithms to trade you have the
ability to go back over trades and strategies in detail and
examine the effectiveness and helpfulness of various inputs
within the model," says Richard Wilson, a consultant with the
Hedge Fund Group. "This is very hard to do with more qualitative
or subjective investment research and decision making." Kumaresan
agrees, saying that machines are good at analysing the
effectiveness of trading strategies. "Even if you are a skilled
trader, you are not going to be able to analyse some of these
things as well as a machine, which can produce a more exact
estimate," he says.

It is the human/machine interaction - not the machine on its own
- that ultimately determines the performance of a particular
trading model. Machines are an enabler, albeit a very useful and
increasingly important one, and for that reason Eaton says we are
likely to continue to see the use of computer modelling for
trading and risk management, as well as the increased use of
algorithmic trading. And as advances in technology make it easier
and more cost effective to deploy machines, they are likely to be
more widely used. "It's clear that humans are using machines more
and more," says Hansbo of Tbricks. "It's also becoming more of a
reality as there are new powerful systems available that you can
use without making huge investments in development
infrastructure."

Crossover

"Man versus machine" is actually a far more fluid situation
that the tag implies; in fact there's migration in both
directions. As the race to minimise latency approaches its zero
sum conclusion, some automated traders are looking for
alternatives; just being fast is no longer necessarily enough
to ensure their profitability. "We've noticed an interesting
shift in the past three months," says David Knox, CEO of
i-traders.com. "Before, the majority of our research clients
were manual traders who were mostly operating intraday, but not
at high frequency. Now we've noticed automated traders wanting
to take an XML feed of our trade ideas and market levels -
either to trade direct or as input to their own models."

The intriguing thing is that a significant number of these
automated traders had a track record in very high frequency
trading but were clearly looking to diversify into higher time
frames and new methods. "Some of them also wanted to take a
feed from us that they could use as a manual overlay to their
automated models," says Knox. "By the same token, some of our
manual trading clients have also moved in the opposite
direction and started dipping a toe into automated trading."

The overall I-TRADERS' perspective is that the recent market
upheavals have precipitated a more agnostic approach by
traders, hence this migration between camps. "In the past,
traders tended to classify themselves as automated or manual
and also by the time frame in which they operated," says
co-founder of I-TRADERS, Shaun Downey. "Now circumstances have
driven a more pragmatic and flexible approach: 'Whatever works,
I'll do that!'"